Imaging system design begins with the focal plane. Assume the focal plane has pixels of a size p, with a size of the image on the focal plane being d. The number n of pixels is n=d/p. The N.A. or numerical aperture roughly determines the field of view of the system, and is defined as N.A.=n0 sin θ, where n0 is the refractive index of the medium through which the light has traveled. Assuming the medium is air and the small angle approximation is valid, n0≈1, so N.A.=sin θ. The angular resolution is Δθp=p/f due to the focal plane and Δθλ=λ/d due to the diffraction limit. Since f and d are related by the N.A., Δθp/Δθλ=N.A.p/λ. Thus, for a given focal plane, the angular resolution is inversely proportional to f, meaning that the thicker the system, the better the angular resolution. Additionally, the size and number of pixels determine the spatial resolution.
If p could be reduced by a desired scaling factor, then f could be reduced by an order of magnitude, while maintaining the resolution of the system. However, there are limits on the ability to enhance imaging by simply increasing the density of pixels. Further, post-processors cannot process information that has not been captured by the imaging system. Additionally, increased optical performance often means increased complexity.
Thus, techniques other than simply increasing pixel density and reliance on post processing are needed to advance imaging systems. Desired advances include reducing camera thickness, improving resolution and improving data efficiency.
Current attempts to achieve these advances include integrated computational imaging systems (ICIS). The design of ICIS simultaneously considers optics, optoelectronics and signal processing, rather than independently designing the optics. System performance for the ICIS is realized through joint optimization of optics, focal plane optoelectronics and post-detection algorithms. The computational imaging used to balance processing between optics and electronics are typically classified into three categories: wavefront encoding, multiplex imaging and feature extraction. Wavefront encoding involves modifying the wavefront phase at or near the pupil plane of an imaging system. In multiplex imaging, typically the optics introduce redundant information used in post-processing detection. In direct feature extraction, feature extraction estimates are made of transform coefficients that are then used to make a decision. Often, all three categories are employed.
Typically, ICIS use non-focal sensors, e.g., interferometric systems, wavefront coded systems. The purposeful blurring attendant with such non-focal sensors, which is then removed in post-processing, provides multiplexing in the optical field. However, this blurring does not exploit the point of high field entropy of the system. For conventional imaging of remote objects, highest entropy is at the focal plane. Thus, rather than using the information inherent in one-to-one mapping, i.e., that there is a relationship between spatially separated regions, the detectors of these systems are acting as pixel sensors rather than image sensors.